Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Chapter 7. Fuzzy Systems

In the previous chapter, we saw an overview of the theory and techniques for building intelligent systems that are capable of processing natural language input. It is certain that there will be a growing demand for machines that can interact with human beings via natural language. In order for the systems to interpret the natural language input and react in the most reasonable and reliable way, the systems need a great degree of fuzziness. The biological brain can very easily deal with approximations in the input compared to the traditional logic we have built with computers. As an example, when we see a person, we can infer the quotient of oldness without explicitly knowing the age of the person. For example, if we see a a two-year-old baby, on the oldness quotient, we interpret the baby as not old and hence young. We can easily deal with the ambiguity in the input. In this case, we do not need to know the exact age of the baby for a fundamental and very basic interpretation...